Score-oriented loss (SOL) functions

Author name not available (Why is that?)

Publication date: 29 March 2021

Abstract: Loss functions engineering and the assessment of forecasting performances are two crucial and intertwined aspects of supervised machine learning. This paper focuses on binary classification to introduce a class of loss functions that are defined on probabilistic confusion matrices and that allow an automatic and a priori maximization of the skill scores. The performances of these loss functions are validated during the training phase of two experimental forecasting problems, thus showing that the probability distribution function associated with the confusion matrices significantly impacts the outcome of the score maximization process.

Has companion code repository: https://github.com/cesc14/SOL

This page was built for publication: Score-oriented loss (SOL) functions

Report a bug (only for logged in users!)Click here to report a bug for this page (MaRDI item Q6363992)